Overview

Data available through: 2020-07-03

Total Cases and Total Deaths

These show the cumulative total of cases and deaths by day. I have also denoted the current cumulative totals. These total values are important; however they are not helpful for figuring out whether the pandemic is slowing down or growing as it is difficult to see trends in cumulative curves like these.


New Cases

Looking at new cases each day can help us see if the pandemic is slowing. A decreasing number of new cases per day is evidence that the pandemic is slowing down.

There can be a lot of variability in the daily case totals due to a variety of variables. One example is the availability of tests; cases will go down if there is a scarcity of tests and rise dramatically when more tests become available. One way to help get a better sense of the overall trend is by smoothing the data using a moving average.

The trends and raw data show a peak around mid-April and had been moving downward likely due to strict lock-down and social distancing measures. In late June, new cases started to increase dramatically, forming a second peak as many states start re-opening and many people stop practicing social distancing measures. There is also a cyclical nature to the daily new cases with counts often being lower on weekends and higher on weekdays.


New Deaths and Death Percentage

COVID-19 is much deadlier than the common flu. One way to measure the impact is to look at the death percentage, which is the total number of deaths divided by the total number of cases.

A big concern during April was that the death percentage was continually increasing, even when actual deaths per day were not increasing. Starting in early May the death percentage started to plateau around 6%. At the end of may, the death percentage starts curving downward as deaths per day continued to decrease as new cases per days continued to plateau and then increase.

Similar to new cases there is a cyclical nature to spikes in new cases. These spikes may be due to reporting times where counts on weekdays are often higher than those on weekends. Spikes also occur when previous deaths are re-assigned as COVID-19 related deaths, such as counting nursing home deaths and/or pneumonia deaths.


Values for Past 14 Days

The actual values for the previous 14 days are detailed in the table below.

Date Total Cases Total Deaths New Cases New Cases 7-Day MA New Deaths New Deaths 7-Day MA Death Percentage
Fri, Jul 03, 2020 2,777,448 128,582 54,917 46,613 703 893 4.630%
Thu, Jul 02, 2020 2,722,531 127,879 54,323 45,309 575 883 4.697%
Wed, Jul 01, 2020 2,668,208 127,304 49,611 43,131 702 888 4.771%
Tue, Jun 30, 2020 2,618,597 126,602 46,248 41,102 1,310 896 4.835%
Mon, Jun 29, 2020 2,572,349 125,292 39,048 39,487 322 826 4.871%
Sun, Jun 28, 2020 2,533,301 124,970 39,849 38,105 259 832 4.933%
Sat, Jun 27, 2020 2,493,452 124,711 42,296 36,250 2,380 833 5.002%
Fri, Jun 26, 2020 2,451,156 122,331 45,790 34,739 633 573 4.991%
Thu, Jun 25, 2020 2,405,366 121,698 39,081 32,618 616 590 5.059%
Wed, Jun 24, 2020 2,366,285 121,082 35,407 30,916 752 602 5.117%
Tue, Jun 23, 2020 2,330,878 120,330 34,944 29,350 823 607 5.162%
Mon, Jun 22, 2020 2,295,934 119,507 29,373 27,858 367 599 5.205%
Sun, Jun 21, 2020 2,266,561 119,140 26,863 26,316 265 604 5.256%
Sat, Jun 20, 2020 2,239,698 118,875 31,721 25,402 561 615 5.308%

Growth Factor

Data available through: 2020-07-03

Is the pandemic slowing?

One important calculation is the growth factor, as outlined in 3Blue1Brown’s youtube video on exponential growth . The growth factor is calculated as follows:

\[ \text{Growth Factor} = \frac{ \text{New-Cases}_N}{\text{New-Cases}_{N-1}} \] where \(N\) is a given day. Essentialy this is taking the amount of new cases today and dividing them by the amount of new cases yesterday.

The growth factor can be very helpful in determining if the pandemic is slowing. If the growth factor is less than 1, this means that the amount of new cases today is less than yesterday. Once there are multiple days with a growth factor less than 1 it is a strong sign that the pandemic is slowing down.

Adjustment to Growth Factor

What if there were 0 cases yesterday? This would make the growth factor undefined (or \(\infty\) according to R). This makes it difficult to look at trends. I have adjusted the growth factor so that if the previous day had 0 cases, the current day’s growth factor is equal to the number of new cases:

\[ \text{Growth Factor} = \begin{cases} \frac{ \text{New-Cases}_N}{\text{New-Cases}_{N-1}} & \text{if } \text{New-Cases}_{N-1} \neq 0 \\[1ex] \text{New-Cases}_N & \text{if } \text{New-Cases}_{N-1} = 0 \end{cases} \] I made this adjustment for the early or late stages of the pandemic when the number of cases per day are 0, 1, or 2. However, given the test scarcity and reporting times there are situations in counties or states where there are 0 cases one day and then hundreds or thousands the next day. This large variability causes spikes in the growth factor in some plots.

Growth Factor Plot

Similar to the new cases per day, there can be a lot of variability in growth factors In order to get a better sense of the trend I am showing a 14-day moving average of the growth factor.

The growth factor shows a different trend than new cases. Here, the growth factor has stayed around 1 since mid-April. Compare that to the new cases plot on the Overview tab, which shows a downward trend after a peak in mid-April. The growth factor remaining around 1 may be due to the cyclical nature of new cases being reported (high during the week, low during the weekends) - but it could also be showing that although the decrease in new cases is a positive sign, we are not out of the woods yet.


Recent Values by Day for US

The actual values for the previous 14 days are detailed in the table below.

Date Total Cases New Cases New Cases 7-Day MA Growth Factor Growth Factor 7-day MA
Fri, Jul 03, 2020 2,777,448 54,917 46,613 1.01 1.03
Thu, Jul 02, 2020 2,722,531 54,323 45,309 1.09 1.05
Wed, Jul 01, 2020 2,668,208 49,611 43,131 1.07 1.05
Tue, Jun 30, 2020 2,618,597 46,248 41,102 1.18 1.05
Mon, Jun 29, 2020 2,572,349 39,048 39,487 0.98 1.05
Sun, Jun 28, 2020 2,533,301 39,849 38,105 0.94 1.06
Sat, Jun 27, 2020 2,493,452 42,296 36,250 0.92 1.05
Fri, Jun 26, 2020 2,451,156 45,790 34,739 1.17 1.06
Thu, Jun 25, 2020 2,405,366 39,081 32,618 1.1 1.06
Wed, Jun 24, 2020 2,366,285 35,407 30,916 1.01 1.06
Tue, Jun 23, 2020 2,330,878 34,944 29,350 1.19 1.06
Mon, Jun 22, 2020 2,295,934 29,373 27,858 1.09 1.08
Sun, Jun 21, 2020 2,266,561 26,863 26,316 0.85 1.05
Sat, Jun 20, 2020 2,239,698 31,721 25,402 1.03 1.04

State Plots

Data available through: 2020-07-03

Case Curves by State



Percentage Change of Weekly Average from Week to Week

These plots are based on NPR’s plots using percentage change in new cases. I also used the same colors. NPR plots can be found here.



Recent Growth Factor by State

Since most of the COVID-19 measures are enacted by individual states, it may be more helpful for an individual to see the growth factor for the last 14 days in a specific state.


Data

Data available through: 2020-07-03

This data is downloaded from USA Facts. I use two of the three datasets available: total cases and total deaths. Both of these datasets are broken down by state and county. This data requires additional formatting, calculation, and aggregation. USA Facts gets data by county on a daily basis, this is totaled to get values for each day for individual states and the entire US.

The American CDC links to USA Facts under Cases & Death by County, which is how I found the data source.

Many of the plots have been restricted to show data on March 15, 2020 and after. This is when case numbers started to rise and preventative measures started to increase dramatically.

Data Limitations

A large limitation for this data is that reported new cases (and thus the growth factor) may not consistently and accurately represent the true number of new cases each day. As mentioned before, this could be due to test availability, reporting protocols, and a number of other variables. It is important to note that this information is a helpful tool in trying to understand the pandemic, but it may not reflect the entire story.